Automated recommendations today are frequently based on an analysis of implicit patterns of user behavior and are reflected to users in an obscure manner, such as, for example, recommendations in a form such as “people who viewed this also viewed,” that are currently presented to viewers on many e-commerce sites today. This type of information is of limited use to a viewer, in that it reduces the likelihood that the viewer will truly relate to the recommendation, since they do not know who the other “viewers” are, and do not know why what the others viewed is relevant to their own specific case. In another example, the present viewer may be presented with average ratings that other viewers gave to a certain product. In this situation, the present viewer does not know if the rating of the other viewers is really relevant for them, as the interest or needs of the other viewers may be quite different from the interests or needs of the present viewer.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of such systems with some aspects of the present invention as set forth in the remainder of the present application with reference to the drawings.